StarSpace VS pykeen

This completely misunderstands what this is.

In this context "knowledge graph" means a representation of knowledge independent of it's serialization format (ie, RDF vs whatever).

It's usable wherever you have a key value (key:value of relationship) or a triple (key:relationship:something).

This is probably 85% of everything that is stored in a database, with the exception being paths that vary enough that you can't use the path as a key or relationship. In particular, most trees are suitable if you use collapse the path down to a single relationship.

I've done a bunch of work on graph embedding. They are very effective for use in anything that can be thought of as a recommendation system ("this person would like these books") or similarity ("this person is similar to these people").

Back when I was working on them I found Starspace wonderfully easy and effective: https://github.com/facebookresearch/StarSpace

In general, treating entity embedding as a standard NLP problem, and relying on their decades old techniques to create embeddings / reductions, is fairly easy, pareto-efficient for first deploy, and can use a lot of the existing infra and software. In essence, this approach is not so far away from the more advanced https://github.com/facebookresearch/StarSpace

QuatE is implemented in PyKEEN, a library for KnowEdge graph EmbeddiNgs, https://github.com/pykeen/pykeen